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							Improve fairness<br>
							of AI systems
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							Fairlearn is an open-source, community-driven project to help
							data scientists improve fairness of AI systems.
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							Learn about AI fairness from our guides and use cases.
							Assess and mitigate fairness issues using our Python toolkit.
							Join our community and contribute metrics, algorithms, and other resources.	
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  					<h6 class="text-left pb-1">FAIRNESS IS SOCIOTECHNICAL</h6>
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						Fairness of AI systems is about more than simply running lines of code.
						In each use case, both societal and technical aspects
						shape who might be harmed by AI systems and how.
						There are many complex sources of unfairness and a variety of societal and technical processes for mitigation,
						not just the mitigation algorithms in our library.
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						Throughout this website, you can find resources on how to think about fairness as sociotechnical,
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						while considering the AI system's broader societal context.
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					<h6 class="text-left pb-1">USE CASE&ensp;|&ensp;CREDIT-CARD LOANS</h6>
					<h2 class="text-left pt-2 pb-3 dark">Assessment and mitigation of fairness issues in credit-card default models</h2>
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						When making a decision to approve or decline a loan, financial services organizations use a variety of models, including a model that predicts the applicant's probability of default.
						These predictions are sometimes used to automatically reject or accept an application, directly impacting both the applicant and the organization.
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						In this scenario, fairness-related harms may arise when the model makes more mistakes for some groups of applicants compared to others.
						We use Fairlearn to assess how different groups, defined in terms of their sex, are affected and how the observed disparities may be mitigated.
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						<a class="quickstart-btn mb-5 px-3 text-center" href="https://fairlearn.org/main/auto_examples/plot_credit_loan_decisions.html">Jupyter Notebook</a>
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		    	<h2 class="pt-3 pb-3 text-left dark">Get started with Fairlearn</h2>
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				    To get started, install the Fairlearn package.
				    But the process does not end there!
				    See our user guide and other resources to understand what fairness means for your use case.
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				    If you run into any issues, reach out on <a href="https://discord.gg/R22yCfgsRn">Discord</a>.</p>
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				    Fairlearn can be installed with pip from PyPI as follows
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						pip install fairlearn
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							User Guide
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						Learn more about fairness in AI, fairness metrics, and mitigation algorithms.
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